Methods for Prediction of Peptide Binding to MHC Molecules: A Comparative Study

Yu, Kun; Petrovsky, Nikolai; Schönbach, Christian; Koh, Judice L.Y.; Brusic, Vladimir

doi:10.1007/bf03402006

Cited by 133 publications

(111 citation statements)

References 45 publications

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“…This SVM was trained using MHCPEP and SYFPEITHI. As a result, it showed very high performance, better than SVMHC (53) and YKW0201 (90). In Formulae 2, 3 formulae from each group are exemplified and the correspondence of energetic terms among them can be seen beyond the groups, as indicated with solid lines.…”

Section: Empirical Scoring Function Methodsmentioning

confidence: 99%

Prediction of T-Cell Epitope

Tsurui¹,

Takahashi²

2007

J Pharmacol Sci

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Abstract. The prevailing methods to predict T-cell epitopes are reviewed. Motif matching, matrix, support vector machine (SVM), and empirical scoring function methods are mainly reviewed; and the thermodynamic integration (TI) method using all-atom molecular dynamics (MD) simulation is mentioned briefly. The motif matching method appeared first and developed with the increased understanding of the characteristic structure of MHC-peptide complexes, that is, pockets aligned in the groove and corresponding residues fitting on them. This method is now becoming outdated due to the insufficiency and inaccuracy of information. The matrix method, the generalization of interaction between pockets of MHC and residues of bound peptide to all the positions in the groove, is the most prevalent one. Efficiency of calculation makes this method appropriate to scan for candidates of T-cell epitopes within whole expressed proteins in an organ or even in a body. A large amount of experimental binding data is necessary to determine a matrix. SVM is a relative of the artificial neural network, especially direct generalization of a linear Perceptron. By incorporating non-binder data and adopting encoding that reflects the physical properties of amino acids, its performance becomes quite high. Empirical scoring functions apparently seem to be founded on a physical basis. However, the estimates directly derived from the method using only structural data are far from practical use. Through regression with binding data of a series of ligands and receptors, this method predicts binding affinity with appropriate accuracy. The TI method using MD requires only structural data and a general atomic parameter, that is, force field, and hence theoretically most consistent; however, the extent of perturbation, inaccuracy of the force field, the necessity of an immense amount of calculations, and continued difficulty of sampling an adequate structure hamper the application of this method in practical use.

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Section: Empirical Scoring Function Methodsmentioning

confidence: 99%

Prediction of T-Cell Epitope

Tsurui¹,

Takahashi²

2007

J Pharmacol Sci

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“…However, the fact that ANNs are dominated by the remaining methods is notable, as they have been the most widely used method in the MHC -peptide binding setting. While ANNs, in turn, dominate simple motif-based classifiers (Brusic et al (1997);Yu et al (2002)), our results indicate that they are not competitive with more credible approaches such as SVMs and ensemble methods. This poor performance is compounded by the tuning sensitivity and black-box nature of ANNs.…”

Section: Discussionmentioning

confidence: 73%

Prediction of Genomewide Conserved Epitope Profiles of HIV-1: Classifier Choice and Peptide Representation

Xiao¹,

Segal²

2005

Statistical Applications in Genetics and Molecular Biology

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Identification of peptides binding to Major Histocompatibility Complex (MHC) molecules is important for accelerating vaccine development and improving immunotherapy. Accordingly, a wide variety of prediction methods have been applied in this context. In this paper, we introduce (tree-based) ensemble classifiers for such problems and contrast their predictive performance with forefront existing methods for both MHC class I and class II molecules. In addition, we investigate the impact of differing peptide representation schemes on performance. Finally, classifier predictions are used to conduct genomewide scans of a diverse collection of HIV-1 strains, enabling assessment of epitope conservation. We investigated all combinations of six classification methods (classification trees, artificial neural networks, support vector machines, as well as the more recently devised ensemble methods (bagging, random forests, boosting) with four peptide representation schemes (amino acid sequence, select biophysical properties, select quantitative structure-activity relationship (QSAR) descriptors, and the combination of the latter two) in predicting peptide binding to an MHC class I molecule (HLA-A2) and MHC class II molecule (HLA-DR4). Our results show that the ensemble methods are consistently more accurate than the other three alternatives. Furthermore, they are robust with respect to parameter tuning. Among the four representation schemes, the amino acid sequence representation gave consistently (across classifiers) best results. This finding obviates the need for feature selection strategies incurred by use of biophysical and/or QSAR properties. We obtained, and aligned, a diverse set of 32 HIV-1 genomes and pursued genomewide HLA-DR4 epitope profiling by querying with respect to classifier predictions, as obtained under each of the four peptide representation schemes. We validated those epitopes conserved across strains against known T-cell epitopes. Once again, amino acid sequence representation was at least as effective as using properties. Assessment of novel epitope predictions awaits experimental verification.

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“…Specialized web-based servers and analysis software allow a statistical prediction of characteristics, such as cellular location, hydrophobicity, secondary and tertiary structure, and potential epitopes (Zhang et al, 1998;Yu et al, 2002).…”

Section: Discussionmentioning

confidence: 99%

A cross-reactive neisserial antigen encoded by the NMB0035 locus shows high sequence conservation but variable surface accessibility

Arenas

Abel

Sánchez

et al. 2008

Journal of Medical Microbiology

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The meningococcal NMB0035 locus encodes a 47 kDa outer-membrane protein that is highly conserved antigenically, and is able to induce antibodies during infection and bactericidal responses in vitro. This study analysed the surface exposure of this protein using specific antibodies in flow cytometry assays and determined its nucleotide sequence in 33 Neisseria strains. Genomic analyses revealed no significant differences in the nucleotide or amino acid sequences, but flow cytometry showed that surface accessibility was highly variable among the strains. These results suggest that masking by and/or association with lipo-oligosaccharides or other membrane molecules can be crucial for antigen accessibility, which must be thoroughly analysed in new vaccine candidates.

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Methods for Prediction of Peptide Binding to MHC Molecules: A Comparative Study

Cited by 133 publications

References 45 publications

Prediction of T-Cell Epitope

Prediction of T-Cell Epitope

Prediction of Genomewide Conserved Epitope Profiles of HIV-1: Classifier Choice and Peptide Representation

A cross-reactive neisserial antigen encoded by the NMB0035 locus shows high sequence conservation but variable surface accessibility

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